航空遥感图像中的目标计数：应用于野生动物和海洋哺乳动物
Object counting from aerial remote sensing images: application to wildlife and marine mammals
AI总结：
针对航空遥感图像中的野生动物计数问题，提出基于UNet和多种骨干网络的高斯密度图回归方法，无需检测器即可计数，在非洲象和海豚数据集上验证了有效性，助力野生动物保护。
摘要：
人为活动对野生动物和海洋动物群构成威胁，促使需要高效的动物计数方法。本研究利用深度学习技术来自动化计数任务。受先前关于人群和动物计数研究的启发，实现了一个带有多种骨干网络的UNet模型，该模型使用高斯密度图进行训练，从而无需训练检测器。新模型被应用于航空图像中海豚和大象的计数任务。定量评估显示出有前景的结果，其中EfficientNet-B5骨干网络在非洲大象计数上取得最佳性能，而ResNet18骨干网络在海豚计数上表现最佳。尽管图像背景复杂，该模型仍能准确定位动物。通过利用人工智能，本研究为野生动物保护工作做出贡献，并通过无需检测的航空遥感高效目标计数，增强人类与野生动物之间的共存。
英文摘要：
Anthropogenic activities pose threats to wildlife and marine fauna, prompting the need for efficient animal counting methods. This research study utilizes deep learning techniques to automate counting tasks. Inspired by previous studies on crowd and animal counting, a UNet model with various backbones is implemented, which uses Gaussian density maps for training, bypassing the need of training a detector. The new model is applied to the task of counting dolphins and elephants in aerial images. Quantitative evaluation shows promising results, with the EfficientNet-B5 backbone achieving the best performance for African elephants and the ResNet18 backbone for dolphins. The model accurately locates animals despite complex image background conditions. By leveraging artificial intelligence, this research contributes to wildlife conservation efforts and enhances coexistence between humans and wildlife through efficient object counting without detection from aerial remote sensing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
